Silvia Cascianelli

31 papers receiving 536 citations

Hit Papers

From Show to Tell: A Survey on Deep Learning-Based Image ...2022202620232024202250100150

Peers

Silvia Cascianelli
Comparison fields: 5 of 93
  • Computer Vision and Pattern Recognition 346
  • Artificial Intelligence 168
  • Aerospace Engineering 93
  • Electrical and Electronic Engineering 51
  • Control and Systems Engineering 47
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Chaoyou Fu China
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Loc Tran United States
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Countries citing papers authored by Silvia Cascianelli

Since Specialization
Citations

This map shows the geographic impact of Silvia Cascianelli's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Silvia Cascianelli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Silvia Cascianelli more than expected).

Fields of papers citing papers by Silvia Cascianelli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Silvia Cascianelli. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Silvia Cascianelli. The network helps show where Silvia Cascianelli may publish in the future.

Co-authorship network of co-authors of Silvia Cascianelli

This figure shows the co-authorship network connecting the top 25 collaborators of Silvia Cascianelli. A scholar is included among the top collaborators of Silvia Cascianelli based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Silvia Cascianelli. Silvia Cascianelli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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From Show to Tell: A Survey on Deep Learning-Based Image Captioningbreakdown →
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Right putamen and age are the most discriminant features to diagnose Parkinson's disease by using 123I-FP-CIT brain SPET data by using an artificial neural network classifier, a classification tree (ClT).
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The classification tree (CIT) classifier applied to 123I-MIBG cardiac scintigraphy in differentiating parkinson's disease (PD) from parkinsonisms (P).
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About Silvia Cascianelli

Silvia Cascianelli is a scholar working on Computer Vision and Pattern Recognition, Statistics, Probability and Uncertainty and Human-Computer Interaction, having authored 33 papers that have together received 548 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (8 papers), Handwritten Text Recognition Techniques (7 papers) and Advanced Image and Video Retrieval Techniques (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (346 citations), Health Informatics (10 citations) and Artificial Intelligence (168 citations). Silvia Cascianelli has collaborated with scholars based in Italy, United States and Belgium. Frequent co-authors include Rita Cucchiara, Lorenzo Baraldi, Marcella Cornia, Mario Luca Fravolini, Matteo Stefanini, Giuseppe Fiameni, Gabriele Costante, Paolo Valigi, Thomas A. Ciarfuglia and Davide Astolfi. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Industrial Informatics and Pattern Recognition Letters.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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2026